Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy
Abstract Background With advances in science and technology, the application of artificial intelligence in medicine has significantly progressed. The purpose of this study is to explore whether the k-nearest neighbors (KNN) machine learning method can identify three milling states based on vibration...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | European Journal of Medical Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40001-023-01154-y |
| _version_ | 1797789833265938432 |
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| author | Rui Wang He Bai Guangming Xia Jiaming Zhou Yu Dai Yuan Xue |
| author_facet | Rui Wang He Bai Guangming Xia Jiaming Zhou Yu Dai Yuan Xue |
| author_sort | Rui Wang |
| collection | DOAJ |
| description | Abstract Background With advances in science and technology, the application of artificial intelligence in medicine has significantly progressed. The purpose of this study is to explore whether the k-nearest neighbors (KNN) machine learning method can identify three milling states based on vibration signals: cancellous bone (CCB), ventral cortical bone (VCB), and penetration (PT) in robot-assisted cervical laminectomy. Methods Cervical laminectomies were performed on the cervical segments of eight pigs using a robot. First, the bilateral dorsal cortical bone and part of the CCB were milled with a 5 mm blade and then the bilateral laminae were milled to penetration with a 2 mm blade. During the milling process using the 2 mm blade, the vibration signals were collected by the acceleration sensor, and the harmonic components were extracted using fast Fourier transform. The feature vectors were constructed with vibration signal amplitudes of 0.5, 1.0, and 1.5 kHz and the KNN was then trained by the features vector to predict the milling states. Results The amplitudes of the vibration signals between VCB and PT were statistically different at 0.5, 1.0, and 1.5 kHz (P < 0.05), and the amplitudes of the vibration signals between CCB and VCB were significantly different at 0.5 and 1.5 kHz (P < 0.05). The KNN recognition success rates for the CCB, VCB, and PT were 92%, 98%, and 100%, respectively. A total of 6% and 2% of the CCB cases were identified as VCB and PT, respectively; 2% of VCB cases were identified as PT. Conclusions The KNN can distinguish different milling states of a high-speed bur in robot-assisted cervical laminectomy based on vibration signals. This method is feasible for improving the safety of posterior cervical decompression surgery. |
| first_indexed | 2024-03-13T01:56:12Z |
| format | Article |
| id | doaj.art-2e542824600a462f99e9d1b21111ee8d |
| institution | Directory Open Access Journal |
| issn | 2047-783X |
| language | English |
| last_indexed | 2024-03-13T01:56:12Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj.art-2e542824600a462f99e9d1b21111ee8d2023-07-02T11:11:15ZengBMCEuropean Journal of Medical Research2047-783X2023-06-012811810.1186/s40001-023-01154-yIdentification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomyRui Wang0He Bai1Guangming Xia2Jiaming Zhou3Yu Dai4Yuan Xue5Key Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General HospitalKey Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General HospitalTianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai UniversityKey Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General HospitalTianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai UniversityKey Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General HospitalAbstract Background With advances in science and technology, the application of artificial intelligence in medicine has significantly progressed. The purpose of this study is to explore whether the k-nearest neighbors (KNN) machine learning method can identify three milling states based on vibration signals: cancellous bone (CCB), ventral cortical bone (VCB), and penetration (PT) in robot-assisted cervical laminectomy. Methods Cervical laminectomies were performed on the cervical segments of eight pigs using a robot. First, the bilateral dorsal cortical bone and part of the CCB were milled with a 5 mm blade and then the bilateral laminae were milled to penetration with a 2 mm blade. During the milling process using the 2 mm blade, the vibration signals were collected by the acceleration sensor, and the harmonic components were extracted using fast Fourier transform. The feature vectors were constructed with vibration signal amplitudes of 0.5, 1.0, and 1.5 kHz and the KNN was then trained by the features vector to predict the milling states. Results The amplitudes of the vibration signals between VCB and PT were statistically different at 0.5, 1.0, and 1.5 kHz (P < 0.05), and the amplitudes of the vibration signals between CCB and VCB were significantly different at 0.5 and 1.5 kHz (P < 0.05). The KNN recognition success rates for the CCB, VCB, and PT were 92%, 98%, and 100%, respectively. A total of 6% and 2% of the CCB cases were identified as VCB and PT, respectively; 2% of VCB cases were identified as PT. Conclusions The KNN can distinguish different milling states of a high-speed bur in robot-assisted cervical laminectomy based on vibration signals. This method is feasible for improving the safety of posterior cervical decompression surgery.https://doi.org/10.1186/s40001-023-01154-yCervical laminectomyVibration signalsFast Fourier transformArtificial intelligenceK-nearest neighbors |
| spellingShingle | Rui Wang He Bai Guangming Xia Jiaming Zhou Yu Dai Yuan Xue Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy European Journal of Medical Research Cervical laminectomy Vibration signals Fast Fourier transform Artificial intelligence K-nearest neighbors |
| title | Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy |
| title_full | Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy |
| title_fullStr | Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy |
| title_full_unstemmed | Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy |
| title_short | Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy |
| title_sort | identification of milling status based on vibration signals using artificial intelligence in robot assisted cervical laminectomy |
| topic | Cervical laminectomy Vibration signals Fast Fourier transform Artificial intelligence K-nearest neighbors |
| url | https://doi.org/10.1186/s40001-023-01154-y |
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